Yanwei Fu | Computer Science | Best Researcher Award

Yanwei Fu | Computer Science | Best Researcher Award

Dr Yanwei Fu ,Fudan University ,China

Based on the comprehensive profile provided, Prof. Dr. Yanwei Fu is indeed a highly qualified candidate for the Best Researcher Award. His achievements, research contributions, and academic standing illustrate his significant impact on the field of computer science, particularly in machine learning and computer vision.

Publication profile

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Educational Background

Prof. Fu holds a Ph.D. in Computer Science with a specialization in Computer Vision from Queen Mary University of London (2011-2014). His earlier education includes a Master’s degree from Nanjing University and a Bachelor’s degree from Nanjing University of Technology. This solid foundation in computer science has equipped him with the skills necessary for cutting-edge research.

Awards and Recognitions

Prof. Fu’s accolades demonstrate his prominence in the academic community:

  • DECRA Fellow by the Australian Research Council (2016)
  • Distinguished Professor of Eastern Scholar at Shanghai Institutions of Higher Learning (2017)
  • 1000 Young Innovative Talent Professional Fellow by NSFC (2018)
  • Winner of the Best Paper Award at the IEEE International Conference on Multimedia and Expo (ICME) (2019)
  • Recipient of the ACM China SIGAI Rising Star Award (2018) and ACM Shanghai Rising Star Award (2019)
  • Fellow of the British Computer Society (2022)

These recognitions highlight his significant contributions to research and academia, establishing him as a leader in his field.

Research Interests

Prof. Fu’s research interests span various crucial topics in machine learning and computer vision:

  1. Learning from Small Samples: He focuses on statistical sparsity and has developed methods for one-shot and few-shot learning.
  2. 3D/4D Object Reconstruction: His work includes innovative techniques for 3D model reconstruction and robotic grasping.
  3. Artificial Intelligence and Generative Models: He explores foundation models for image manipulation and advanced applications in robotic tasks.

His diverse research portfolio reflects his commitment to advancing knowledge in these areas, driving innovation through interdisciplinary approaches.

Positions Held

Prof. Fu’s career includes prestigious positions such as:

  • DECRA Fellow (2017-2020)
  • Visiting Professor at renowned institutions, including Tencent AI Lab and AItricks.com
  • Associate Professor and Professor at Fudan University since 2016

These roles not only underscore his expertise but also his ability to contribute to and lead significant research projects.

Selected Publications

Prof. Fu has authored numerous influential papers, contributing substantially to the field. Some notable publications include:

  • “Pixel2mesh: Generating 3D Mesh Models from Single RGB Images” – A pioneering work in 3D modeling.
  • “An End-to-End Architecture for Class-Incremental Object Detection with Knowledge Distillation” – Recognized with a best paper award, showcasing his innovative approach to object detection.

Publication Top Notes

Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers

Pixel2mesh: Generating 3d mesh models from single rgb images

Soft filter pruning for accelerating deep convolutional neural networks

Transductive Multi-view Zero-Shot Learning

Pose-normalized image generation for person re-identification

Chained-tracker: Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking

Multi-scale deep learning architectures for person re-identification

Multi-level semantic feature augmentation for one-shot learning

Transductive multi-view embedding for zero-shot recognition and annotation

Conclusion

Given his extensive educational background, numerous prestigious awards, impactful research interests, and significant contributions to the field of computer vision, Prof. Dr. Yanwei Fu stands out as an exemplary candidate for the Best Researcher Award. His work not only advances theoretical knowledge but also has practical implications that push the boundaries of current technology.

Syed MuhammadMohsin | Computer Science Award | Best Researcher Award

Syed MuhammadMohsin | Computer Science Award | Best Researcher Award

Mr. Syed MuhammadMohsin,Ā Syed Muhammad Mohsin, Pakistan

šŸ‘Øā€šŸŽ“Ā Syed Muhammad Mohsin, a dedicated PhD scholar at COMSATS University Islamabad, Pakistan, focuses on energy-efficient cloud technologies. With expertise in computer science and a solid academic background, including MS and BS degrees, he has published extensively in renowned journals and presented at international conferences. Mohsin’s research delves into topics like IoT network security, renewable energy forecasting, and smart grid management. Alongside his scholarly pursuits, he serves as an Assistant Technical Officer at the Pakistan Atomic Energy Commission and holds visiting lecturer positions at various universities. His multifaceted skills encompass coding, network administration, and project management.

Publication Top Notes

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Google Scholor

Education

Mr.Syed Muhammad Mohsin, a dedicated scholar šŸŽ“, embarked on his academic journey by earning a Bachelor’s degree in Computer Science from Virtual University of Pakistan. He furthered his knowledge with a Master’s degree from the same institution, delving into the intricacies of Service Oriented Architecture for Cloud of Things. Driven by his passion for research, he pursued a PhD from COMSATS University Islamabad, focusing on energy-efficient cloud migration. His academic odyssey, complemented by numerous publications and professional experiences, reflects his unwavering commitment to advancing knowledge in computer science and technology.

Research Focus

Mr.Syed Muhammad Mohsin is a highly accomplished PhD scholar specializing in the field of energy-efficient cloud computing. With a strong background in computer science and extensive experience in academia and industry, Mohsin’s research focus lies at the intersection of green computing and intelligent migration strategies for traditional energy sources. His work, highlighted by numerous publications and contributions to prestigious conferences, demonstrates a deep understanding of emerging technologies like IoT, AI, and blockchain, applied to energy forecasting, network optimization, and security. Mohsin’s expertise and dedication make him a valuable asset in shaping the future of sustainable computing. šŸŒ±šŸ’»

Publication Top Notes

  1. A survey on deep learning methods for power load and renewable energy forecasting in smart microgridsĀ šŸ“Š
    • Authors:Ā S. Aslam, H Herodotou, SM Mohsin, N Javaid, N Ashraf, S Aslam
    • Journal:Ā Renewable and Sustainable Energy Reviews
    • Cited by:Ā 355
    • Year:Ā 2021
  2. AI-empowered, blockchain and SDN integrated security architecture for IoT network of cyber physical systemsĀ šŸ”’
    • Authors:Ā S Latif, S. A., XianWen, F. B., Iwendi, C., Wang, F. L., Mohsin, S. M., Han …
    • Journal:Ā Computer Communications
    • Cited by:Ā 163
    • Year:Ā 2021
  3. A fair pricing mechanism in smart grids for low energy consumption usersĀ šŸ’”
    • Authors:Ā K Aurangzeb, S Aslam, SM Mohsin, M Alhussein
    • Journal:Ā IEEE Access
    • Cited by:Ā 49
    • Year:Ā 2021
  4. A comprehensive review of computing paradigms, enabling computation offloading and task execution in vehicular networksĀ šŸš—
    • Authors:Ā A Waheed, MA Shah, SM Mohsin, A Khan, C Maple, S Aslam, …
    • Journal:Ā IEEE Access
    • Cited by:Ā 45
    • Year:Ā 2022
  5. Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage systemĀ šŸ”‹
    • Authors:Ā K Aurangzeb, S Aslam, SI Haider, SM Mohsin, S Islam, HA Khattak, …
    • Journal:Ā Transactions on Emerging Telecommunications Technologies
    • Cited by:Ā 32
    • Year:Ā 2022
  6. Performance analysis of hybridization of heuristic techniques for residential load schedulingĀ āš”
    • Authors:Ā Z Iqbal, N Javaid, SM Mohsin, SMA Akber, MK Afzal, F Ishmanov
    • Journal:Ā Energies
    • Cited by:Ā 29
    • Year:Ā 2018
  7. Deep learning based techniques to enhance the performance of microgrids: a reviewĀ šŸ”„
    • Authors:Ā S Aslam, H Herodotou, N Ayub, SM Mohsin
    • Conference:Ā 2019 International Conference on Frontiers of Information Technology (FIT ā€¦)
    • Cited by:Ā 27
    • Year:Ā 2019